Shillong, Feb 4: The Department of Information Technology at North-Eastern Hill University (NEHU), has developed an AI-based Landslide Susceptibility Map (LSM) of Meghalaya using an ensemble machine learning framework.
The framework combines ten different machine learning models to enhance accuracy, robustness and reliability of the LSM.
Meghalaya’s complex geological structure, frequent seismic activity and intense rainfall during monsoons, makes the state highly susceptible to landslides, resulting in loss of life and properties every year. According to experts, the impact of landslides can be reduced by identifying and regularly monitoring the vulnerable areas, NEHU said in a press release today.
Roads were identified as a big factor in landslide occurrence. This is attributed to slope destabilisation during road construction, alteration of natural drainage patterns and disturbance caused by vehicle movements. Other influential causative factors include slope degree, NDVI, soil type, elevation, road density and lithology, the release said.
The research was carried out by K Amitab and his team with financial support from the central government’s Science and Engineering Research Board (SERB). Historical landslide inventory data obtained from the Geological Survey of India (GSI) and the North Eastern Space Applications Centre (NESAC) were used to train and evaluate the model. The framework achieved an accuracy exceeding 90 percent, NEHU added, demonstrating its effectiveness in predicting landslide-prone zones.
The generated LSM classifies landslide susceptibility of Meghalaya into five risk categories: very high, high, moderate, low and very low. According to the map, approximately 7 percent of the state falls under the very high-risk category, while 6 percent, 8 percent, 19 percent and 60 percent fall under the high, moderate, low and very low categories, respectively.
East Khasi Hills is the most vulnerable region, with approximately 730 square km falling under the very high risk category. Other vulnerable districts include Ri-Bhoi, Eastern West Khasi Hills, West Khasi Hills, South West Khasi Hills, East Jaintia Hills and West Jaintia Hills.
The LSM can serve as a valuable tool for disaster management agencies in prioritising resource allocation to high-risk regions and guiding proactive planning to mitigate the impact of landslides, NEHU concluded.
